The ability to predict human behaviour to changes in their environment is of interest to decision makers in areas as diverse as retail, human resources and even traffic planning. For example, a product manager for a telecommunications company would like to know what the changes in market share would be, should he change the monthly plan fee or the number of calls cap. Armed with this information he could inform the current business strategy, which might be to increase market share, make more profit or some specific mix of objectives. The example described above could be answered through traditional market research approach known as A-B testing where each of the possible combinations of the price and plan attributes are presented to a target group and a count of the preference recorded. This count is taken as proxy for market share and a business strategy formed from it.
However, in all but trivial cases, there are many more attributes of interest than can be effectively studied in such traditional market research approaches. In the example above, it would be reasonable to also include the attributes of brand, handset, warranty, insurance, SMS cost, data plan and many more. In reality, the combinatorial mathematics involved precludes an exhaustive survey of alternatives as the number of combinations rapidly approaches and exceeds the number of people on the planet.
To predict human behaviour, a different approach is required, one where an understanding the factors driving choice is involved and a model of the decision making process is made. In this approach, model predictions stand in for frequency counts and statistical methods tell us how ‘good’ predictions from the model are. In recent years this approach has begun to be formalised into a related set of econometric methods known collectively as Choice Modelling (or sometimes Choice Experiments, the two terms will be used interchangeably).
The principal output of a choice modelling approach is a mathematical model capturing the essence of human decision making processes (as probabilistic decision rules, from which a numeric utility can be calculated). The model can be interrogated to make hard probabilistic predictions and validated to demonstrate how accurate predictions are. To generate a choice model a controlled experiment is often employed to explore preference for hypothetical options. Through judicious selection of these hypothetical options, via an experimental design, only a small subset of the vast numbers of combinations of attributes needs to be explored to create a valid choice model from which predictions can be made.
The intermediate steps of experimental design and modelling require a very high level of specialised skill in economics and mathematics. In addition the technical steps required to deploy a choice experiment and collect data require some technical skill. A layman, that is the typical person, product manager, decision maker or non expert user who wishes to understand their relevant market typically possesses none of these skills. Consequently the layman must employ or one or more specialists and go through all the manual steps of communicating, exploring and managing his requirements with the various specialists necessary.
The practical reality of using choice modelling is that it is a costly and time-consuming process with many of the activities outside of the control of the layman instigator. Furthermore, many interesting and useful applications of choice modelling are precluded because of the attendant costs and timeframes imposed because of the necessity of employing specialist guidance. While it is both feasible and desirable to predict human behaviour accurately with choice modelling, in its current state it could not be described as a single technology. It is instead an assemblage of academic theories and methods, manual procedures and disparate software components. Thus as a result, even when a layman employs specialist consultants, the lack of appropriately developed tools and methodologies, often forces the layman instigator into performing a much more limited study than they may otherwise desired, if at all.
There is thus a need to develop both individual components and an overall system to enable a lay person to implement choice experiments (or choice modelling), or at least to provide them with a useful alternative.